Regina Barzilay

Delta Electronics Professor, EECS.

Faculty Co-Lead, J-Clinic

MacArthur Fellow

MIT Computer Science & Artificial Intelligence Lab

32 Vassar Street, 32-G468

Cambridge, MA 02139

(617) 258-5706

regina@csail.mit.edu

Announcements

J-Clinic AICURES initiative on COVID: on demand therapeutic design

Learn about our efforts in drug discovery and participate.

News

Machine learning has been used to automatically translate long-lost languages

Some languages that have never been deciphered could be the next ones to get the machine translation treatment.

Can these researchers catch cancer much earlier than ever before?

From revolutionizing the mammogram to spotting a single tumor cell in the blood...

Looking to Technology to Avoid Doctors’ Offices and Emergency Rooms

Americans are eagerly turning to the latest tech devices in hopes of preventing and detecting medical problems early...

From Gene Editing to A.I., How Will Technology Transform Humanity?

Five big thinkers — Regina Barzilay, George Church, Jennifer Egan, Catherine Mohr and Siddhartha Mukherjee — puzzle over the future of the future.

Automating molecule design to speed up drug development

Machine-learning model could help chemists make molecules with higher potencies, much more quickly.

Research Interests

NLP

Natural Language Processing

I develop machine learning models that aim to understand and generate natural languages. We are currently witnessing the first generation of NLP tools that have been deployed at scale and are used by millions of people. However, the major component of this success is access to large amounts of training data which machines use to learn mappings between input and output. In many applications and languages, such annotations are not readily available, and are expensive and slow to collect. I am interested in designing algorithms that do not suffer from this annotation dependence. Specifically, we are developing deep learning models that can transfer annotations across domains and languages, that can learn from a few annotated examples by utilizing supplementary data sources, and that can take advantage of human-provided rationales to constrain model structure.

Oncology

Learning to Cure

Data collected about millions of cancer patients — their pathology slides, imaging, and other tests — contain answers to many open questions in oncology. Jointly with the MGH collaborators, we are developing algorithms that can learn from this data to improve models of disease progression, prevent over-treatment, and narrow down to the cure. On the NLP side, we are creating databases which record pertinent cancer features extracted from raw documents. On the computer vision side, we are working on deep learning models that compute personalized assessment from mammogram data focusing on early cancer detection.

Chemistry

ML Drug Discovery

Today, drug discovery involves practitioners with years of advanced training and is carried out in a trial-and-error, labor-intensive fashion. Our goal is to change a traditional pipeline. In a joint work with chemical engineers and chemists at MIT, we are working on deep learning methods for modeling chemical processes.

Papers

A Deep Learning Approach to Antibiotic Discovery

Jonathan M. Stokes, Kevin Yang, Kyle Swanson, Wengong Jin, Andres Cubillos-Ruiz, Nina M. Donghia, Craig R. MacNair, Shawn French, Lindsey A. Carfrae, Zohar Bloom-Ackerman,..., Tommi S. Jaakkola, Regina Barzilay, James J. Collins

Cell, 2020.

Analyzing Learned Molecular Representations for Property Prediction

Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi Jaakkola, Klavs Jensen, Regina Barzilay

Journal of Chemical Information and Modeling, 2019.

Neural Decipherment via Minimum-Cost Flow: from Ugaritic to Linear B

Jiaming Luo, Yuan Cao, Regina Barzilay

Proceedings of ACL, 2019.

A Deep Learning Mammography-Based Model for Improved Breast Cancer Risk Prediction

Adam Yala, Constance Lehman, Tal Schuster, Tally Portnoi, Regina Barzilay

Radiology, 2019.

Cross-Lingual Alignment of Contextual Word Embeddings, with Applications to Zero-shot Dependency Parsing

Tal Schuster, Ori Ram, Regina Barzilay and Amir Globerson

Proceedings of NAACL, 2019.

GraphIE: A Graph-Based Framework for Information Extraction

Yujie Qian, Enrico Santus, Zhijing Jin, Jiang Guo and Regina Barzilay

Proceedings of NAACL, 2019.

Inferring Which Medical Treatments Work from Reports of Clinical Trials

Eric Lehman, Jay DeYoung, Regina Barzilay and Byron C. Wallace

Proceedings of NAACL, 2019.

Learning Multimodal Graph-to-Graph Translation for Molecule Optimization

Wengong Jin, Kevin Yang, Regina Barzilay, and Tommi Jaakkola

Proceedings of ICLR, 2019.

Path-Augmented Graph Transformer Network

Benson Chen, Regina Barzilay, Tommi Jaakkola

ICML Workshop on Learning and Reasoning with Graph-Structured Representations, 2019.

Generative Models for Graph-Based Protein Design

John Ingraham, Vikas K. Garg, Regina Barzilay, Tommi Jaakkola

ICLR Workshop on Deep Generative Models for Highly Structured Data, 2019

A Graph-Convolutional Neural Network Model for the Prediction of Chemical Reactivity.

Connor W. Coley, Wengong Jin, Luke Rogers, Timothy F. Jamison, Tommi S. Jaakkola, William H. Green, Regina Barzilay, and Klavs F. Jensen

Chemical Science 10, no. 2 (2019): 370-377.

A Deep Learning Model to Triage Screening Mammograms: A Simulation Study

Adam Yala, Tal Schuster, Randy Miles, Regina Barzilay, and Constance Lehman

Radiology, 2019

Deep Learning Model to Assess Cancer Risk on the Basis of a Breast MR Image Alone

Tally Portnoi, Adam Yala, Tal Schuster, Regina Barzilay, Brian Dontchos, Leslie Lamb, and Constance Lehman

American Journal of Roentgenology, 2019.

Junction Tree Variational Autoencoder for Molecular Graph Generation

Wengong Jin, Regina Barzilay, and Tommi Jaakkola

Proceedings of International Conference on Machine Learning (ICML), 2018

Deriving Machine Attention from Human Rationales

Yujia Bao, Shiyu Chang, Mo Yu, and Regina Barzilay

Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2018.

Multi-Source Domain Adaptation with Mixture of Experts

Jiang Guo, Darsh Shah, and Regina Barzilay

Proceedings of Empirical Methods in Natural Language Processing (EMNLP), 2018

Mammographic Breast Density Assessment Using Deep Learning: Clinical Implementation

Constance D Lehman, Adam Yala, Tal Schuster, Brian Dontchos, Manisha Bahl, Kyle Swanson, and Regina Barzilay

Radiology, 2018.

Do Neural Information Extraction Algorithms Generalize Across Institutions?

Enrico Santus, Clara Li, Adam Yala, Donald Peck, Rufina Soomro, Naveen Faridi, Isra Mamshad, Rong Tang, Conor R. Lanahan, Regina Barzilay, and Kevin Hughes

JCO Clinical Informatics, 2019.

View all

Bio

Regina Barzilay is a Delta Electronics professor in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology. Her research interests are in natural language processing, applications of deep learning to chemistry and oncology. She is a recipient of various awards including the NSF Career Award, the MIT Technology Review TR-35 Award, Microsoft Faculty Fellowship and several Best Paper Awards at NAACL and ACL. In 2017, she received a MacArthur fellowship, an ACL fellowship and an AAAI fellowship. She received her Ph.D. in Computer Science from Columbia University, and spent a year as a postdoc at Cornell University.

Awards

Best Paper Award, HLT/NAACL 2004

Technology Research News: “Top Picks: Technology Research Advances of 2004”

NSF Career Award 2005

Technology Review: 35 Top Innovators 2005

IEEE Intelligent Systems: “AI Ten to Watch” 2006

Microsoft Faculty Fellowship 2006

Ross Career Development Professor 2006

Best Paper Award, ACL 2009

Carolyn Baldwin Morrison lecture, Cornell 2009

Best Paper Award, SLT 2010

Best Student Paper Award, NAACL 2014

Faculty Research Innovation Fellowship 2014

Best Paper Honorable Mention, EMNLP 2015

Delta Electronics Professor 2015

Burgess & Elizabeth Jamieson Award for Excellence in Teaching 2016

Best Paper Award, EMNLP 2016

MacArthur Fellowship 2017

ACL Fellowship 2017

AAAI Fellowship 2018

Top 100 AI Leaders in Drug Discovery & Advanced Healthcare 2019